bert-multilingual-passage-reranking-msmarco

Maintainer: amberoad

Total Score

71

Last updated 5/17/2024

📉

PropertyValue
Model LinkView on HuggingFace
API SpecView on HuggingFace
Github LinkNo Github link provided
Paper LinkNo paper link provided

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Model overview

The bert-multilingual-passage-reranking-msmarco model is a multilingual BERT-based passage reranking model developed by Amberoad. This model can be used to improve the relevance of search results by re-scoring passages based on how well they match a given query. The model is built on top of BERT with a densely connected neural network that takes the 768-dimensional [CLS] token as input and outputs a single value between -10 and 10, indicating how well the passage matches the query.

The model is trained on the Microsoft MS Marco Dataset, which contains approximately 400 million query-passage pairs. This dataset covers over 100 different languages, allowing the model to perform passage reranking across a wide range of languages.

Compared to similar multilingual models like multilingual-e5-base and multilingual-e5-large, the bert-multilingual-passage-reranking-msmarco model is more specialized for the task of passage reranking. Its architecture is tailored for this specific task, rather than being a more general-purpose text embedding model.

Model inputs and outputs

Inputs

  • Search query: A text query for which the model will re-score and rank relevant passages.
  • Passage: A text passage that the model will evaluate for relevance to the given query.

Outputs

  • Relevance score: A single numerical value between -10 and 10, indicating how well the passage matches the query. Higher scores indicate a better match.

Capabilities

The bert-multilingual-passage-reranking-msmarco model can be used to improve the relevance of search results by re-scoring passages based on their match to the query. This can be particularly useful for applications like web search, enterprise search, or question answering, where retrieving the most relevant information is crucial.

The model's multilingual capabilities allow it to perform this passage reranking task across a wide range of languages, making it a versatile tool for global search and retrieval applications.

What can I use it for?

The bert-multilingual-passage-reranking-msmarco model can be used as a drop-in replacement in the Nboost Library to directly improve the results of Elasticsearch searches. By re-ranking the top retrieved passages based on their relevance to the query, the model can boost the overall quality of the search results by up to 100%.

Additionally, the model could be useful in any application where you need to retrieve and rank relevant passages or documents based on a user query, such as:

  • Question answering systems: Using the model to re-score candidate passages or documents to find the most relevant answers to user questions.
  • Chatbots and virtual assistants: Leveraging the model to improve the relevance of information retrieved in response to user queries.
  • Academic or enterprise search: Enhancing the quality of search results for research papers, internal documents, or other knowledge repositories.

Things to try

One interesting aspect of the bert-multilingual-passage-reranking-msmarco model is its ability to perform passage reranking across a wide range of languages. You could experiment with using the model to improve search results for queries in different languages, and analyze how the performance varies across languages.

Additionally, you could explore combining the model's passage reranking capabilities with other search or retrieval techniques, such as using it in conjunction with traditional search engines or other AI-powered text ranking models. This could lead to even more robust and accurate search and retrieval solutions.



This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!

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